The Use of Virtual Surgical Planning for Reduction Cranioplasty
Bibliographic record
Abstract
Summary: Hydrocephalic macrocephaly may occur as a result of untreated hydrocephalus. Reduction cranioplasty is the treatment of choice for these patients when the weight of their head interferes with normal development and negatively impacts quality of life. However, this procedure has several associated risks, including prolonged anesthesia, significant blood loss, and death. Virtual surgical planning (VSP) has been shown to be a useful adjunct for orthognathic and craniofacial surgery. The following report details the application and advantages of this technology in the setting of a reduction cranioplasty. We report the case of a 2-year-old girl with severe hydrocephalic macrocephaly who underwent a reduction cranioplasty guided by VSP with computer-aided design and manufacturing (CAD/CAM). Prefabricated cutting guides and a concave assembly bowl were used for precise fixation of bony segments. Our patient underwent a successful reduction cranioplasty using VSP and CAD/CAM. This technology allowed precise remodeling of the cranial vault with minimal bony gaps in the final construct. Head circumference and intracranial volume were reduced from 70 cm and 4,575 cm3 to 62 cm and 2,645 cm3, respectively. VSP with CAD/CAM can serve as a useful adjunct in complex cases of cranioplasty allowing for an increase in the precision, the efficacy, and the esthetic result.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".